# Analysis of influencing factors and prediction model for fatigue among medical staff at a large primary centralized medical observation point

## TL;DR

This study identifies factors contributing to fatigue in medical staff at centralized observation points and develops a predictive model to help hospitals create better fatigue prevention strategies.

## Contribution

A combined diagnostic model for predicting fatigue in medical staff, based on factors like night shifts and physical exercise.

## Key findings

- Night shift proportion, physical exercise, and gender are independent factors associated with fatigue.
- The multivariate logistic regression model showed higher predictive accuracy than the univariate model.
- The model provides a basis for targeted fatigue prevention strategies in hospitals.

## Abstract

Long-term closed-loop management and the demanding nature of work at centralized medical observation points can increase fatigue among medical staff, impair physical and mental health, and degrade the quality of medical services. This study aims to evaluate the factors influencing fatigue among medical personnel at a primary centralized medical observation point, including personal conditions and working environment factors.

A total of 145 medical staff members from Lanxi People’s Hospital, all of whom participated in epidemic prevention work at centralized medical observation points from January 2021 to April 2023, were enrolled in this study. Data were analyzed using a binary logistic regression model to evaluate predictive efficacy, with the study design grounded in the ecological systems theory framework.

The proportions of night shifts, participation in physical exercise, and gender were detected as independent factors associated with fatigue (all p < 0.05). The multivariate logistic regression model demonstrated higher predictive accuracy than the univariate model based on the night-shift proportion, indicating that the combined diagnostic model is valuable for diagnosing fatigue among medical staff.

The proportion of night shifts, participation in physical exercise, and gender affect the occurrence of fatigue, and the combined diagnostic model predictive value is beneficial to the diagnosis of fatigue, thereby providing a basis for hospitals to develop targeted fatigue prevention strategies in night shift settings, personnel allocation, and increasing stress reduction measures.